A novel utility metric to measure information loss for generalization and suppression techniques in Privacy Preserving Data publishing

Privacy has become a prime importance in this digital era. Personally Identifiable Information (PII) gets collected in various firms such as educational institutions, government organizations, hospitals etc‥ The collected data is often published and utilized for the purpose of analysis, research, decision making, advertisement or for the purpose of business. It is the duty of the data curator to store and to publish the data safely. A person who is having accessibility to the data that is published must not be able to identify or learn any new personal or sensitive information of an individual. Statistical Disclosure Control(SDC) is a suite of anonymization techniques. The aim of SDC is post processing the data containing sensitive or personal information and effectively protect the privacy of the participating data subject. However these techniques leads to “information loss”, i.e., any analysis or research carried on such a data might not give a appropriate result. This paper aims to discuss various metrics available to assess the information loss in the processed data and an attempt has been made to propose our technique for measuring the loss.